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NVIDIA's cloud GPU technologies are based on the
company's new Kepler GPU architecture, designed for use
in large-scale data centers. Its virtualization
capabilities allow GPUs to be simultaneously shared by
multiple users. NVIDIA claims that its ultra-fast
streaming display capability eliminates lag, making a
remote data center feel like it's just next door. And
its extreme energy efficiency and processing density
lowers data center costs.

"Kepler cloud GPU technologies shifts cloud computing
into a new gear," said Jen-Hsun Huang, NVIDIA president
and chief executive officer. "The GPU has become
indispensable. It is central to the experience of
gamers. It is vital to digital artists realizing their
imagination. It is essential for touch devices to
deliver silky smooth and beautiful graphics. And now,
the cloud GPU will deliver amazing experiences to those
who work remotely and gamers looking to play untethered
from a PC or console."

NVIDIA VGX Platform

The enterprise implementation of Kepler cloud
technologies, the NVIDIA VGX platform, accelerates
virtualized desktops. The platform enables IT
departments to deliver a virtualized desktop with the
graphics and GPU computing performance of a PC or
workstation to employees using any connected device.

With the NVIDIA VGX platform in the data center,
employees can now access a true cloud PC from any device
-- thin client, laptop, tablet or smartphone --
regardless of its operating system. NVIDIA claims that
VGX enables knowledge workers for the first time to
access a GPU-accelerated desktop similar to a
traditional local PC.

Integrating the VGX platform into the corporate network
also enables enterprise IT departments to address the
complex challenges of "BYOD" -- employees bringing their
own computing device to work. It delivers a remote
desktop to these devices, providing users the same
access they have on their desktop terminal.

NVIDIA VGX Boards are designed for hosting large numbers
of users in an energy-efficient way. The first NVIDA VGX
board is configured with four GPUs and 16 GB of memory,
and fits into the standard PCI Express interface in
servers.

NVIDIA VGX GPU Hypervisor is a software layer that
integrates into commercial hypervisors, such as the
Citrix XenServer, enabling virtualization of the GPU.

NVIDIA User Selectable Machines (USMs) is a
manageability option that allows enterprises to
configure the graphics capabilities delivered to
individual users in the network, based on their demands.
Capabilities range from true PC experiences available
with the NVIDIA standard USM to enhanced professional 3D
design and engineering experiences with NVIDIA Quadro or
NVIDIA NVS GPUs.

The NVIDIA VGX platform enables up to 100 users to be
served from a single server powered by one VGX board,
improving user density on a single server compared with
traditional virtual desktop infrastructure (VDI)
solutions.

NVIDIA GeForce GRID

The gaming implementation of Kepler cloud technologies,
NVIDIA GeForce GRID, powers cloud gaming services.
NVIDIA will offer the platform to gaming-as-a-service
providers, who will be able to use it to remotely
deliver gaming experiences, "with the potential to
surpass those on a console," according to the company.

Gamers will be able to play the latest games on any
connected device, including TVs, smartphones and tablets
running iOS and Android.

"Gamers will now have access to seamlessly play the
world's best titles anywhere, anytime, from phones,
tablets, TVs or PCs," said Phil Eisler, general manager
of cloud gaming at NVIDIA. "GeForce GRID represents a
massive disruption in how games are delivered and
played."

The key technologies powering the new platform are
NVIDIA GeForce GRID GPUs with dedicated
ultra-low-latency streaming technology and cloud
graphics software. Gaming-as-a-service providers will be
able to operate scalable data centers at costs that are
in line with those of movie-streaming services.

Using the NVIDIA Kepler architecture, NVIDIA GeForce
GRID GPUs minimize power consumption by simultaneously
encoding up to eight game streams. This allows providers
to scale their service offerings to support millions of
concurrent gamers.

Featuring two Kepler architecture-based GPUs, each with
its own encoder, the processors have 3,072 CUDA
technology cores and 4.7 teraflops of 3D shader
performance. This enables providers to render highly
complex games in the cloud and encode them on the GPU,
rather than the CPU, allowing their servers to
simultaneously run more game streams.

Fast streaming technology reduces server latency to as
little as 10 milliseconds by capturing and encoding a
game frame in a single pass. The GeForce GRID platform
uses fast-frame capture, concurrent rendering and
single-pass encoding to achieve ultra-fast game
streaming.

The latency-reducing technology in GeForce GRID GPUs
compensates for the distance in the network, so "gamers
will feel like they are playing on a gaming
supercomputer located in the same room," NVIDIA claims.

Also at GTC, NVIDIA and Gaikai demonstrated a virtual
game console, consisting of an LG Cinema 3D Smart TV
running a Gaikai application connected to a GeForce GRID
GPU in a server 10 miles away. Lag-free play was enabled
on a highly complex PC game, with only an Ethernet cable
and wireless USB game pad connected to the TV.

A number of gaming-as-a-service providers including
Gaikai, Playcast Media System and Ubitus, announced
their support of the GeForce GRID. EPIC Games, Capcom
and THQ have also announced support for Nvidia's new
technology.

New Tesla GPUs

NVIDIA also today unveiled a new family of Tesla GPUs
based on the company's newest Kepler GPU computing
architecture.

The new NVIDIA Tesla K10 and K20 GPUs are computing
accelerators built to handle complex HPC problems.
Nvidia says that Kepler is three times as efficient as
its predecessor, the NVIDIA Fermi architecture.

NVIDIA developed a set of architectural technologies
that make the Kepler GPUs high performing and highly
energy efficient. Among the major technologies are:

- SMX Streaming Multiprocessor -- The basic building
block of every GPU, the SMX streaming multiprocessor was
redesigned from the ground up for high performance and
energy efficiency. It delivers up to three times more
performance per watt than the Fermi streaming
multiprocessor, making it possible to build a
supercomputer that delivers one petaflop of computing
performance in just 10 server racks. SMX's energy
efficiency was achieved by increasing its number of CUDA
architecture cores by four times, while reducing the
clock speed of each core, power-gating parts of the GPU
when idle and maximizing the GPU area devoted to
parallel-processing cores instead of control logic.

- Dynamic Parallelism -- This capability enables GPU
threads to dynamically spawn new threads, allowing the
GPU to adapt dynamically to the data. It simplifies
parallel programming, enabling GPU acceleration of a
broader set of popular algorithms, such as adaptive mesh
refinement, fast multipole methods and multigrid
methods.

- Hyper-Q -- This enables multiple CPU cores to
simultaneously use the CUDA architecture cores on a
single Kepler GPU. This increases GPU utilization,
slashing CPU idle times and advancing programmability.
Hyper-Q is ideal for cluster applications that use MPI.

Optimized for customers in oil and gas exploration and
the defense industry, a single Tesla K10 accelerator
board features two GK104 Kepler GPUs that deliver an
aggregate performance of 4.58 teraflops of peak
single-precision floating point and 320 GB per second
memory bandwidth.

The NVIDIA Tesla K20 GPU is the new flagship of the
Tesla GPU product family. The Tesla K20 is planned to be
available in the fourth quarter of 2012.

The Tesla K20 is based on the GK110 Kepler GPU. This GPU
delivers three times more double precision compared to
Fermi architecture-based Tesla products and it supports
the Hyper-Q and dynamic parallelism capabilities. The
GK110 GPU is expected to be incorporated into the new
Titan supercomputer at the Oak Ridge National Laboratory
in Tennessee and the Blue Waters system at the National
Center for Supercomputing Applications at the University
of Illinois at Urbana-Champaign.

In addition to the Kepler architecture, NVIDIA today
released a preview of the CUDA 5 parallel programming
platform. Available to the members of NVIDIA's GPU
Computing Registered Developer program, the platform
will enable developers to begin exploring ways to take
advantage of the new Kepler GPUs, including dynamic
parallelism.

The CUDA 5 parallel programming model is planned to be
widely available in the third quarter of 2012.